Identifying spatial domains from spatial transcriptome by graph attention network_Journal of Biomedical Engineering

Authors：

WU Hanwen ,  GAO Jie

Jiangnan University, Wuxi, Jiangsu 214122, P. R. China;

Corresponding author：

GAO Jie, Email: gaojie@jiangnan.edu.cn

Keywords：

Spatial transcriptomics; Graph attention network; Deep learning; Clustering analysis

DOI：

10.7507/1001-5515.202304030

Video：

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Abstract Full text Figures/Tables Video References Cited by

Due to the high dimensionality and complexity of the data, the analysis of spatial transcriptome data has been a challenging problem. Meanwhile, cluster analysis is the core issue of the analysis of spatial transcriptome data. In this article, a deep learning approach is proposed based on graph attention networks for clustering analysis of spatial transcriptome data. Our method first enhances the spatial transcriptome data, then uses graph attention networks to extract features from nodes, and finally uses the Leiden algorithm for clustering analysis. Compared with the traditional non-spatial and spatial clustering methods, our method has better performance in data analysis through the clustering evaluation index. The experimental results show that the proposed method can effectively cluster spatial transcriptome data and identify different spatial domains, which provides a new tool for studying spatial transcriptome data.

Citation： WU Hanwen, GAO Jie. Identifying spatial domains from spatial transcriptome by graph attention network. Journal of Biomedical Engineering, 2024, 41(2): 246-252. doi: 10.7507/1001-5515.202304030 Copy

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12.	Traag V A, Waltman L, van Eck N J. From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports, 2019, 9(1): 5233.
13.	Wolf F A, Angerer P, Theis F J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biology, 2018, 19(1): 15.
14.	Paszke A, Gross S, Massa F, et al. Pytorch: an imperative style, high-performance deep learning library. arXiv preprint, 2019, DOI: 10.48550/arXiv.1912.01703.
15.	Lewis M, Liu Y, Goyal N, et al. Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint, 2019, DOI: 10.48550/arXiv.1910.13461.
16.	Fey M, Lenssen J E. Fast graph representation learning with PyTorch Geometric. arXiv preprint, 2019, DOI: 10.48550/arXiv.1903.02428.
17.	McInnes L, Healy J, Melville J. Umap: uniform manifold approximation and projection for dimension reduction. arXiv preprint, 2018, DOI: 10.48550/arXiv.1802.03426.
18.	Hubert L, Arabie P. Comparing partitions. Journal of Classification, 1985, 2: 193-218.
19.	Xu C, Jin X, Wei S, et al. DeepST: identifying spatial domains in spatial transcriptomics by deep learning. Nucleic Acids Research, 2022, 50(22): e131.
20.	Dong K, Zhang S. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nature Communications, 2022, 13(1): 1739.
21.	Maynard K R, Collado-Torres L, Weber L M, et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nature Neuroscience, 2021, 24(3): 425-436.
22.	Ma S, Skarica M, Li Q, et al. Molecular and cellular evolution of the primate dorsolateral prefrontal cortex. Science, 2022, 377(6614): eabo7257.

1. Blondel V D, Guillaume J L, Lambiotte R, et al. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008: P10008.
2. Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data. Cell, 2019, 177(7): 1888-1902.
3. Zhao E, Stone M R, Ren X, et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nature Biotechnology, 2021, 39(11): 1375-1384.
4. Xu Y, McCord R P. CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis. BMC Bioinformatics, 2021, 22(1): 397.
5. Reynolds D. Gaussian mixture models. Encyclopedia of Biometrics, 2018: 659-663.
6. Yuan Y, Bar-Joseph Z. GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data. Genome Biology, 2020, 21(1): 300.
7. Hu J, Li X, Coleman K, et al. SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nature Methods, 2021, 18(11): 1342-1351.
8. Pham D, Tan X, Xu J, et al. stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues. BioRxiv, 2020, DOI: 10.1101/2020.05.31.125658.
9. Xu H, Fu H, Long Y, et al. Unsupervised spatially embedded deep representation of spatial transcriptomics. Genome Med, 2024, 16(1): 12.
10. Teng H, Yuan Y, Bar-Joseph Z. Clustering spatial transcriptomics data. Bioinformatics, 2022, 38(4): 997-1004.
11. Velickovic P, Cucurull G, Casanova A, et al. Graph attention networks. arXiv preprint, 2018, DOI: 10.48550/arXiv.1710.10903.
12. Traag V A, Waltman L, van Eck N J. From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports, 2019, 9(1): 5233.
13. Wolf F A, Angerer P, Theis F J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biology, 2018, 19(1): 15.
14. Paszke A, Gross S, Massa F, et al. Pytorch: an imperative style, high-performance deep learning library. arXiv preprint, 2019, DOI: 10.48550/arXiv.1912.01703.
15. Lewis M, Liu Y, Goyal N, et al. Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint, 2019, DOI: 10.48550/arXiv.1910.13461.
16. Fey M, Lenssen J E. Fast graph representation learning with PyTorch Geometric. arXiv preprint, 2019, DOI: 10.48550/arXiv.1903.02428.
17. McInnes L, Healy J, Melville J. Umap: uniform manifold approximation and projection for dimension reduction. arXiv preprint, 2018, DOI: 10.48550/arXiv.1802.03426.
18. Hubert L, Arabie P. Comparing partitions. Journal of Classification, 1985, 2: 193-218.
19. Xu C, Jin X, Wei S, et al. DeepST: identifying spatial domains in spatial transcriptomics by deep learning. Nucleic Acids Research, 2022, 50(22): e131.
20. Dong K, Zhang S. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nature Communications, 2022, 13(1): 1739.
21. Maynard K R, Collado-Torres L, Weber L M, et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nature Neuroscience, 2021, 24(3): 425-436.
22. Ma S, Skarica M, Li Q, et al. Molecular and cellular evolution of the primate dorsolateral prefrontal cortex. Science, 2022, 377(6614): eabo7257.

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